Hartley, RichardSchaffalitzky, Frederik2015-12-13June 27 200769521584http://hdl.handle.net/1885/79556We investigate the use of the L∞ cost function in geometric vision problems. This cost function measures the maximum of a set of model-fitting errors, rather than the sum-of-squares, or L 2 cost function that is commonly used (in least-squares fitting).Keywords: Cost functions; Image sequences; Motion recovery; Projection errors; Cameras; Computer software; Data reduction; Matrix algebra; Optimization; Polynomials; Problem solving; Computer vision20042015-12-11